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Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

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Medical LLM - Medium Free trial

Latest Version:
5.4.5
Use for chat, RAG, medical summarization, open-book question answering with context of up to 32K tokens.

    Product Overview

    Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming detailed clinical notes, patient encounters, and various medical reports into concise, digestible summaries. The summarization feature boosts efficiency while preserving critical details, supporting optimal patient care. Its question-answering capability ensures accurate, context-specific responses to both open and closed medical queries, further enhancing decision-making. For physicians, this tool offers a quick grasp of a patient’s medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient’s journey, condition, and treatment protocols swiftly. Optimized for Retrieval-Augmented Generation (RAG), the model can be used in combination with healthcare databases, EHR, and scientific literature repositories (like PubMed) to enhance response quality.

    Key Data

    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Real-Time Inference

      • Instance Type: ml.g6.48xlarge
      • Maximum supported context length for this instance type: 8k
      • Tokens per Second during real-time inference:
        • Summarization: up to 10 tokens per second
        • QA: up to 12 tokens per second
    • Batch Transform

      • Instance Type: ml.g5.48xlarge
      • Maximum supported context length for this instance type: 8k
      • Tokens per Second:
        • Summarization: up to 20 tokens per second
        • QA: up to 50 tokens per second
    • Benchmarking Results:

      • Achieves 86.31% average on OpenMed benchmarks, surpassing GPT-4 (82.85%) and Med-PaLM-2 (84.08%)
      • Performance in medical genetics: 95%; performance in professional medicine: 94.85%
      • Clinical knowledge comprehension 89.81% and college biology mastery 93.75%
      • Achieves 58.9% average on standard LLM benchmarks
      • Balance of specialized medical knowledge and broad language understanding, demonstrated by 70.93% on GPT4All benchmark
      • Achieves 75.54% performance in medical MCQAs and 79.4% on PubMedQA

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$19.96/hr

    running on ml.g6.48xlarge

    Model Batch Transform$19.96/hr

    running on ml.g5.48xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$16.688/host/hr

    running on ml.g6.48xlarge

    SageMaker Batch Transform$20.36/host/hr

    running on ml.g5.48xlarge

    About Free trial

    Try this product for 15 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.g6.48xlarge
    Vendor Recommended
    $19.96

    Usage Information

    Model input and output details

    Input

    Summary

    For a complete description of the input format and parameters go here

    Input MIME type
    application/json, application/jsonlines
    Sample input data

    Output

    Summary

    The output is a JSON object or a set of JSON Lines objects that contain the generated text(s)

    JSON Format { "response": [ "model response for input 1", "model response for input 2", ... ] } JSON Lines (JSONL) Format {"response": "model response for input 1"} {"response": "model response for input 2"}

    The JSON Lines format consists of separate JSON objects, where each object represents a model response for the respective input.

    Output MIME type
    application/json, application/jsonlines
    Sample output data

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Medical LLM - Medium

    For any assistance, please reach out to support@johnsnowlabs.com.

    AWS Infrastructure

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    Refund Policy

    No refunds are possible.

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